# Discrete Time Markov Chains: Joint, Conditional, and Marginal Distributions

Every day for lunch you have either a sandwich (state 1), a burrito (state 2), or pizza (state 3). Suppose your lunch choices from one day to the next follow a MC with transition matrix

\[ \mathbf{P} = \begin{bmatrix} 0 & 0.5 & 0.5\\ 0.1 & 0.4 & 0.5\\ 0.2 & 0.3 & 0.5 \end{bmatrix} \]

Suppose today is Monday and consider your upcoming lunches.

- Monday is day 0, Tuesday is day 1, etc.
- You start with pizza on day 0 (Monday).
- Let \(T\) be the first time (day) you have a sandwich.
- Let \(V\) be the number of times (days) you have a burrito this five-day work week.
- Pizza costs $5, burrito $7, and sandwich $9.

- Compute and interpret in context \(\text{P}(T > 4)\).
- Find the marginal distribution of \(V\), and interpret in context \(\text{P}(V = 2)\).
- Compute the expected total cost of your lunch this work week (Monday through Friday). Interpret this value as a long run average in context.
- Describe in detail how, in principle, you could use physical objects (coins, dice, spinners, cards, boxes, etc) to perform by hand a simulation to approximate \(\text{E}(V|T=4)\). Note: this is NOT asking you to compute \(\text{E}(V|T=4)\) or how you would compute it using matrices/equations. Rather, you need to describe in words how you would set up and perform the simulation, and how you would use the simulation results to approximate \(\text{E}(V|T=5)\).